| | |
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| | | vad_model="fsmn-vad", vad_model_revision="v2.0.2", \ |
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| | | punc_model="ct-punc-c", punc_model_revision="v2.0.2", \ |
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| | | spk_model="cam++", spk_model_revision="v2.0.2") |
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| | | res = model(input=f"{model.model_path}/example/asr_example.wav", |
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| | | batch_size=64, |
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| | | hotword='魔搭') |
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| | | res = model.generate(input=f"{model.model_path}/example/asr_example.wav", |
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| | | batch_size=64, |
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| | | hotword='魔搭') |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download. |
|---|
| | |
|---|
| | | for i in range(total_chunk_num): |
|---|
| | | speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
|---|
| | | is_final = i == total_chunk_num - 1 |
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| | | res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) |
|---|
| | | res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word. |
|---|
| | |
|---|
| | | |
|---|
| | | model = AutoModel(model="fsmn-vad", model_revision="v2.0.2") |
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| | | wav_file = f"{model.model_path}/example/asr_example.wav" |
|---|
| | | res = model(input=wav_file) |
|---|
| | | res = model.generate(input=wav_file) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | ### Voice Activity Detection (Non-streaming) |
|---|
| | |
|---|
| | | for i in range(total_chunk_num): |
|---|
| | | speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
|---|
| | | is_final = i == total_chunk_num - 1 |
|---|
| | | res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) |
|---|
| | | res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) |
|---|
| | | if len(res[0]["value"]): |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | |
|---|
| | | from funasr import AutoModel |
|---|
| | | |
|---|
| | | model = AutoModel(model="ct-punc", model_revision="v2.0.2") |
|---|
| | | res = model(input="那今天的会就到这里吧 happy new year 明年见") |
|---|
| | | res = model.generate(input="那今天的会就到这里吧 happy new year 明年见") |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | ### Timestamp Prediction |
|---|
| | |
|---|
| | | model = AutoModel(model="fa-zh", model_revision="v2.0.2") |
|---|
| | | wav_file = f"{model.model_path}/example/asr_example.wav" |
|---|
| | | text_file = f"{model.model_path}/example/text.txt" |
|---|
| | | res = model(input=(wav_file, text_file), data_type=("sound", "text")) |
|---|
| | | res = model.generate(input=(wav_file, text_file), data_type=("sound", "text")) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | [//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):) |
|---|
| | |
|---|
| | | } |
|---|
| | | @inproceedings{gao22b_interspeech, |
|---|
| | | author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan}, |
|---|
| | | title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}}, |
|---|
| | | title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}, |
|---|
| | | year=2022, |
|---|
| | | booktitle={Proc. Interspeech 2022}, |
|---|
| | | pages={2063--2067}, |
|---|
| | | doi={10.21437/Interspeech.2022-9996} |
|---|
| | | } |
|---|
| | | @inproceedings{shi2023seaco, |
|---|
| | | author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang}, |
|---|
| | | title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}, |
|---|
| | | year={2023}, |
|---|
| | | booktitle={ICASSP2024} |
|---|
| | | } |
|---|
| | | ``` |
|---|
